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Constrained Online Two-stage Stochastic Optimization: Algorithm with (and without) Predictions
When the model parameters are drawn from unknown non-stationary distributions and we are given machine-learned predictions of the distributions, we develop a new algorithm from our framework with a regret $O(W_T+\sqrt{T})$, where $W_T$ measures the total inaccuracy of the machine-learned predictions.
Understanding AI Cognition: A Neural Module for Inference Inspired by Human Memory Mechanisms
How humans and machines make sense of current inputs for relation reasoning and question-answering while putting the perceived information into context of our past memories, has been a challenging conundrum in cognitive science and artificial intelligence.
A hybrid model based on CNN and Bi-LSTM for urban water demand prediction
The training time of CNN-Bi-LSTM is less than LSTM, Bi-LSTM, CNN, and CNN-LSTM, but larger than SAEs.
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